from utils import print_time_report, print_results, plot_results
print_time_report()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0 | 13 | 23 |
| daal4py_KNeighborsClassifier | 0 | 3 | 15 |
| KNeighborsClassifier_kd_tree | 0 | 5 | 41 |
| daal4py_KNeighborsClassifier_kd_tree | 0 | 1 | 30 |
| KMeans | 0 | 19 | 27 |
| daal4py_KMeans | 0 | 6 | 15 |
| total | 0 | 49 | 34 |
print_results(algo="KNeighborsClassifier", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
print_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
from utils import _make_dataset
data = _make_dataset('KMeans', 'daal4py', compare_cols=["n_iter"])
data[data["function"] == "fit"].sort_values(["n_iter_sklearn", "n_iter_daal4py"])
| estimator | lib | function | n_samples | n_features | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score | mean_sklearn | stdev_sklearn | n_iter_sklearn | mean_daal4py | stdev_daal4py | n_iter_daal4py | speedup | stdev_speedup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0431 | 0.0438 | 19.0 | 0.0043 | 0.0010 | 18.0 | 10.02 | 43.80 |
| 9 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.0846 | 0.0333 | 22.0 | 0.0071 | 0.0007 | 30.0 | 11.92 | 47.57 |
| 18 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.4918 | 0.0203 | 30.0 | 0.1087 | 0.0070 | 30.0 | 4.52 | 2.90 |
| 27 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.0161 | 0.0002 | 30.0 | 0.0054 | 0.0006 | 30.0 | 2.98 | 0.33 |
| 36 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0257 | 0.0006 | 30.0 | 0.0056 | 0.0005 | 30.0 | 4.59 | 1.20 |
| 45 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.3649 | 0.0767 | 30.0 | 0.0528 | 0.0013 | 30.0 | 6.91 | 59.00 |
| 54 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.1629 | 0.0072 | 30.0 | 0.0145 | 0.0008 | 30.0 | 11.23 | 9.00 |
| 63 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.2086 | 0.0048 | 30.0 | 0.0285 | 0.0026 | 30.0 | 7.32 | 1.85 |
| 3 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
| 6 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0001 | 0.0001 | NaN | 3.00 | 1.00 |
| 12 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0002 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 2.00 |
| 15 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0001 | 0.0001 | NaN | 3.00 | 1.00 |
| 21 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0009 | 0.0001 | NaN | 0.0008 | 0.0002 | NaN | 1.12 | 0.50 |
| 24 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
| 30 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0002 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 2.00 |
| 33 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0001 | 0.0001 | NaN | 3.00 | 1.00 |
| 39 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 42 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0001 | 0.0001 | NaN | 3.00 | 1.00 |
| 48 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0010 | 0.0003 | NaN | 0.0009 | 0.0001 | NaN | 1.11 | 3.00 |
| 51 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0001 | 0.0001 | NaN | 3.00 | 1.00 |
| 57 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0007 | 0.0002 | NaN | 0.0003 | 0.0001 | NaN | 2.33 | 2.00 |
| 60 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
| 66 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0007 | 0.0002 | NaN | 0.0004 | 0.0001 | NaN | 1.75 | 2.00 |
| 69 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
print_results(algo="KMeans", versus_lib="daal4py", compare_cols=["n_iter"])
plot_results(algo="KMeans", versus_lib="daal4py", group_by_cols=["init", "max_iter", "n_clusters", "n_init", "tol", "function"], compare_cols=["n_iter"])